Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback

Lanqin Zheng, Kaushal Kumar Bhagat, Miaolang Long and Nitesh Kumar Jha

SAGE Open, 2025, vol. 15, issue 3, 21582440251363298

Abstract: Computer-supported collaborative learning (CSCL) has been broadly utilized in the field of education. However, learners often face difficulties in improving CSCL performance, including improved knowledge elaboration, knowledge convergence, and coregulation. Therefore, the present study aims to compare the effects of automated informative feedback and knowledge graph-based suggestive feedback on knowledge elaboration, knowledge convergence, and coregulation. This study adopted a convenience sampling method, and a total of 104 undergraduate students registered in a mandatory course voluntarily participated in a quasiexperimental study. The students in experimental Group 1 adopted knowledge graph-based suggestive feedback, the students in experimental Group 2 adopted automated informative feedback, and the students in the control group adopted traditional online collaborative learning without any feedback. The findings revealed that knowledge graph-based suggestive feedback significantly improved group performance, knowledge elaboration, knowledge convergence, and coregulated behaviors compared to informative feedback and traditional online collaborative learning without any feedback. This study has theoretical and practical implications for feedback design and implementation in CSCL practice.

Keywords: collaborative learning; knowledge elaboration; knowledge convergence; informative feedback; suggestive feedback (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251363298

DOI: 10.1177/21582440251363298

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